FIGURE 1 - uploaded by Qazwan Abdullah
Content may be subject to copyright.
Source publication
The use of Fuzzy Logic Controller (FLC) as speed controller for Induction Motor (IM) drives is garnering strong researchers’ interest since it has proven to achieve superior performance compared to conventional controllers. The aim of this study is to review and investigate the design, operations, and effects of rules reduction for FLC in IM drives...
Contexts in source publication
Context 1
... works by decomposing torque and flux into DQ-frame and with the help of phase transformation and hysteresis control or space vector control, it can generate switching pulses for the inverter, DTC works by using two hysteresis flux and torque controllers to select the most appropriate voltage vector based on a predefined switching table in accordance with to flux position and torque and flux error signals. Fig.1 shows a block diagram of IM drives system consisting of IM model, a speed controller, FOC or DTC drive method, and a Voltage Source Inverter (VSI) [7]. ...
Context 2
... speed performance comparison of the standard 49 rules and simplified 9 rules are presented in Fig.9 at 1400rpm, 900rpm and 700rpm. In addition, Fig.10 shows the speed performance comparison of the standard 25 rules and simplified 7 rules at 1400rpm, 900rpm and 700rpm. ...
Context 3
... addition, Fig.10 shows the speed performance comparison of the standard 25 rules and simplified 7 rules at 1400rpm, 900rpm and 700rpm. Finally, the speed performance of the standard 9 rules and the simplified 5 rules are shown in Fig.11at 1400rpm, 900rpm and 700rpm. ...
Context 4
... works by decomposing torque and flux into DQ-frame and with the help of phase transformation and hysteresis control or space vector control, it can generate switching pulses for the inverter, DTC works by using two hysteresis flux and torque controllers to select the most appropriate voltage vector based on a predefined switching table in accordance with -to flux position and torque and flux error signals. Fig.1 shows a block diagram of IM drive-s system consisting of IM model, a speed controller, FOC or DTC drive method, and a Voltage Source Inverter (VSI) [7]. ...
Context 5
... speed performance comparison of the standard 49 rules and simplified 9 rules are presented in Fig.9 at 1400rpm, 900rpm and 700rpm. In addition, Fig.10 shows the speed performance comparison of the standard 25 rules and simplified 7 rules at 1400rpm, 900rpm and 700rpm. ...
Similar publications
This paper shows the application of fuzzy logic technique based on duty ratio control and contribution of fuzzy logic control to select optimum voltage vector to reduce the ripple in torque in an induction motor. The hybrid technique is used to get the best results over than classic direct torque control (C_DTC). The conventional proportional & int...
Fuzzy logic controller performance depends upon fuzzy rule base. The control strategy is represented by a set of rules which explain basic characteristics of the controller. The fuzzy logic controllers’ inputs and outputs are characterized by its membership function. Most of the researchers use triangular MFs for implementation of control algorithm...
The non-linearity of variable-Reluctance Machine (8/6) and the dependence of machine inductance on rotor position and applied current complicate the development of the control strategies of drives using variable-Reluctance Machine variable-Reluctance Machine (VRM). The classical-control algorithms for example of derived full proportional action may...
Citations
... FLC constitutes a control system grounded in fuzzy logic, a mathematical framework designed to manage uncertainty and imprecision. In contrast to traditional control systems governed by precise binary logic, fuzzy logic accommodates degrees of truth, proving especially beneficial in scenarios without clear definition [132]. ...
... It excels in situations where the development of precise mathematical models may be challenging. FLCs have diverse applications across fields like control systems, robotics, and artificial intelligence (AI), offering a versatile and intuitive approach to decision-making in dynamic environments [132,133]. ...
... This research did not use systematic algorithms to come to their conclusions; instead, they used uncertain approaches to choose the dominant rules. To estimate the variations in induction motor stator resistance caused by temperature fluctuations, a fuzzy based resistance estimator has been reported in [47]. It is clear from the results of the two estimators that the PI-based resistance estimator does not provide as good tracking as the fuzzy logic-based estimator does. ...
div align="center">
In this paper, hybrid models based on fuzzy systems and neural networks are reviewed. A fuzzy inference system is explicitly represented by expertise for induction motor drives, incorporating the learning capability of artificial neural networks. Researchers have been attracted to neuro-fuzzy techniques for training and inference in induction motor drives due to their efficiency. According to the classification of research articles from 2000 to 2020, this article presents a review of different artificial neural network techniques, fuzzy and neuro-fuzzy systems. The main objective is to provide a concise overview of current neuro-fuzzy research and to enable readers to identify appropriate methods according to their research interests.
</div
... It plays an essential part in several practical applications and has many fuzzy inference mechanisms [67]. Both the Mamdani and Takagi-Sugeno methods are of wide interest in various scientific fields [68]- [70]. Mamdani-type is selected in this study as it is computationally proficient and more compact. ...
This paper purposes to demonstrate the effectiveness of fuzzy logic controller (FLC) over proportional integral (PI) controller for reducing the fault current and maintaining the voltage profile at different faults conditions using Static Synchronous Compensator (STATCOM) which is considered an effective FACTS (Flexible Alternating Current Transmission System) device. The study evaluates the
performance of a power system equipped with STATCOM which is connected in shunt with bus B1 under various faults conditions, including single-phase and three-phase faults. The performance of the STATCOM is evaluated by using two
different controllers: PI controllers and FLCs. A comparative analysis is done between the performances of the two different controllers using Matlab/Simulink software package. The results obtained conclude that the presented system gives better performance with STATCOM as compared to not using it under several faults conditions besides, the STATCOM gives better response with FLC as compared to PI controller. It is demonstrated that STATCOM with FLC can reduce the positive sequence fault current at bus B1 to 96.49% of its value without using STATCOM under line to ground fault and 98.17% under three line to ground fault whereas STATCOM with PI controller can reduce it to (99.57, 99.05%), respectively. Also, the bus voltage B1 is improved to 102.19% by using STATCOM with fuzzy controller under line to ground fault and 101.86% under three line to ground fault whereas STATCOM with PI controller can improve it to (100.21, 100.93%), respectively.
... However, it's important to note that these advanced approaches often come with substantial computational requirements, raising concerns regarding practical implementation and potential hardware costs. Additionally, research has been dedicated to the simplification and optimization of rule-bases within FLCs [23]. This approach aims to facilitate the design and implementation of complex control systems while minimizing the computational burden. ...
This paper presents the simulation and experimental results of speed control of a three-phase Permanent Magnet Synchronous Motor (PMSM). The control techniques employed in this work include a Proportional Integral (PI) controller and a Takagi-Sugeno Fuzzy Logic Controller (TS-FLC). While theory and simulation help in predicting the PI controller settings for real drive implementation, further adjustment of the PI controller coefficients is still needed for optimal performance. However, the complexity of PI controller tuning can be overcome by employing a fuzzy logic controller, which is less affected by variations in model parameters and load torque. Two variants of the proposed TS-FLC are implemented: a simple algorithm with 9 rules and a standard algorithm with 49 rules. The drive system utilizes Field Oriented Control (FOC). The control program is developed using the C/C++ programming language and executed on the Texas Instruments TMS320LF28335 Digital Signal Processor (DSP), known for its intelligent motor control capabilities. The drive system evaluation was performed through simulations and experimentation using the MCK28335 professional development kit provided by Technosoft Company. The simulation and experimental results of the proposed controllers are compared under various conditions, including speed reversal and load torque variations. Index Terms-Digital Signal Processor (DSP) real implementation, field oriented speed control, fuzzy logic controller, Permanent Magnet Synchronous Motor (PMSM)
... One of the main concerns is the higher computational time, which can limit its real-time applicability in some cases. To address this issue, FLC is often integrated with other control techniques and used in offline mode [168]. By combining the strengths of FLC with other control methods, engineers can leverage the advantages of both approaches while mitigating the computational time limitations of FLC as illustrated in Table 14. ...
... The control design of a DC-DC converter using a fuzzy logic controller is shown in Fig. 15. FLC is a useful tool in a variety of applications because of its adaptability and capacity to manage challenging and uncertain circumstances, offering [168,170,174] effective and intelligent control solutions for numerous systems and processes. ...
In recent times, the increasing demand for energy consumption on a global scale and the prevalent use of power electronics DC-DC converters in various applications, such as hybrid energy systems, hybrid vehicles, aerospace, satellites, and portable electronic devices. To increase the converters’ dependability, effectiveness, adaptability, and cost-effectiveness, they have undergone substantial research and development. Diverse input levels can be merged for diverse output levels using new converter topologies, which increase their possibilities from single input-single output setups to multi-input-multiple configurations. The goal of ongoing research is to lower costs and component counts while raising efficiency and dependability. This review paper focuses on the analysis of non-isolated converters, especially for SI-MIMO topologies produced from different converter types. The research work, also includes control techniques and methods like proportional integral derivative, sliding mode control, model predictive control, state-space modelling, fuzzy logic control, and maximum power point tracking, which are all integrated with non-isolated DC-DC converters while considering things like settling concerns, response time, and complexity.
... Because the machine is exposed to many external disturbances and its behaviour is highly non-linear, conventional PI controllers offer mediocre performance in dynamic control, as they have well-known drawbacks, like the exact dependence of the system parameters and the sensitivity to perturbations. [11]. To overcome these limitations, predictive current control presents a very good alternative. ...
... Lastly, converting fuzzy outputs into crisp values is called defuzzification process. Figure 5 shows the diagram of FLC operations [49]. The FLC inputs are the system errors, and they are assigned to several partial MFs. ...
... The behaviors of the system have to be wellknown by the expert, so MFs can accurately designed. The MFs have a very important role in the FLC design for both inputs and outputs, this is because the fuzzy rules are tuned based on the designed MFs in the IF-THEN form to express the possibilities of the inputs and decides the appropriate outputs [49]. Figure 6 shows the designed MFs of the HM for both inputs and outputs of the RL and the FL. ...
... Figure 6 shows the designed MFs of the HM for both inputs and outputs of the RL and the FL. The triangular MFs are advantageous compared to the order MFs types as they have better computational effectiveness and are simple in terms of tuning [49]. Thus, the FLC is designed in this research based on the triangular MFs. ...
Flexible links possess numerous advantages over rigid links which impose the research attention towards employing flexible links in robotics. However, flexible links have a tip vibration that has the difficulty of being presented in dynamic models. Furthermore, the tip vibration leads to the control difficulty as a controller must control a system for the position and the tip elastic vibration. This research proposes a dynamic model and an intelligent hybrid optimal controller of a hybrid manipulator, the hybrid manipulator has a rigid link, a flexible link, and a variable payload. The proposed dynamic model is obtained using a combined technique of the finite element method and the Lagrangian method. The proposed dynamic model is compared with a SimMechanics model in terms of their open-loop responses, and both models have close match responses which indicates that the novel dynamic model is accurate. The intelligent hybrid optimal controller is also proposed based on the integration of an optimal linear quadratic regulator and a fuzzy logic controller. The integration of the fuzzy control and the linear quadratic regulator controller achieves 39.5% reduction of the fuzzy rules which assists to solve the explosion of the fuzzy rules problem since the hybrid manipulator is a multi-input system. The proposed intelligent optimal controller demonstrates improved position control, effective tip vibration suppression, better tip trajectory tracking, and better robustness to overcome the impact of the payload uncertainty compared to the linear quadratic regulator controller based on the simulation validation of the Simulink in MATLAB.
... A back-toback converter connects the rotor winding of the wound rotor induction generator (DFIG) to the grid. This converter, which is bidirectional, is composed of two voltage-source converters that are linked to one another by a single DC link [10]. Those two converters behave as inverter and rectifier and vice versa according to the power's flow throughout the rotor winding [11]. ...
... The idea behind vector control is to operate the DFIG as a separate excitation DC machine with a naturally occurring decoupling between the excitation current that generates flux and the leading current that generates electromagnetic torque.This decoupling offers excellent performance, a wide speed control range, and a very quick torque response. [10,18]. The Stator Flux orientation is based on the alignment of the stators flux with the rotating frame (d) axis. ...
... The fuzzy logic control system, which uses this idea to mimic human decision-making, has greatly advanced as a result. Later in 1975, Ebrahim Mamdani developed linguistic synthesis control rules based on knowledgeable human operators and launched the fuzzy inference method to regulate a steam engine and boiler [10]. An artificial intelligence controller called a fuzzy logic controller (FLC) mimics human reasoning when it comes to operating systems or processes. ...
One of the common control algorithms used to
maintain the dynamic and steady state of the DFIG at an
acceptable level of stability is the conventional indirect field
vector control applied to wind energy conversion systems based
doubly fed induction generator. The conventional proportional
-integral controller used in doubly fed induction generator
machines has numerous drawbacks, including its reliance on the
machine's mathematical model and parameters to define the
value of its gains, which disallows it from functioning properly
in the scenario of unpredictably changing parameter values. In
this paper, fuzzy logic is introduced to the proportional -integral
controller to enhance the performance of the doubly fed
induction generator. Fuzzy logic mimics human analysis of a
situation and decision-making by choosing the most appropriate
action to take. The design of fuzzy controller is based on taking
the error and its derivative as in inputs to generate the output
The Wind Energy Conversion System based doubly fed
induction generator comparative analysis results for the
conventional controller and the fuzzy controller showed
satisfactory performances in terms of power quality, robustness,
and precision in the case of using the fuzzy controller.
... Regarding the computational burden and the practical implementation, several applications are depicted in the recent literature [46][47][48]. In ref [46], the simplification rules method being a popular technique is employed to reduce the FLC computational burden. ...
... As a result, the number of fuzzy rules is minimized with minimal effect on the fuzzy variables coverage and the output decisions. However, elimination of some fuzzy rules may still lead to incomplete linguistic instructions for eliminating the static error [47]. Another technique to reduce the computational burden in relation to real-time implementation is depicted in references [49][50][51], which consists of appropriate selection of fuzzy membership functions (MFs) with simple equations for both fuzzification and defuzzification process. ...
... In this part, with the help of dSPACE 1104 control desk, the execution time (computational burden) of both TS-FLC and M-FLC are computed in Fig. 15. M-FLC utilizes higher execution time compared to TS-FLC, where 0.62 ms and 0.88 ms are, respectively, recorded for TS-FLC and M-FLC [45][46][47]. As expected, the higher execution time of M-FLC controller increases the computational burden for implementing FLC in real. ...
... I NDUCTION motor, by virtue of its rugged design, low cost, wide adaptability, and high reliability, has become one of the most important workhorses in the industry [1], [2], [3]. Due to the wide application of induction motors, their failure could cause severe impacts on the production of industrial enterprises, which may lead industrial enterprises to incur huge losses and even catastrophic accidents. ...
Due to the strong relationship between the frequencies of sideband harmonics (SBHs) and the slip of induction motors, if the frequencies of SBHs can be efficiently and precisely obtained from the spectrum of the stator current (SC) signal, the slip of induction motors, which is a key factor for motor current signature analysis, can be estimated more conveniently, compared with slip estimation through rotor slot or eccentricity harmonics. However, the spectral leakage of fundamental frequency component (FFC) of the SC signal during Fourier spectrum analysis, which is mainly caused by fluctuation of the fundamental frequency, often covers up the SBHs, imposing great difficulty on identification of SBHs. Herein, a novel self-adaptive Wiener filter (WF) is proposed to suppress the FFC of the SC signal. Because of the inherent consistency between the frequencies of the FFCs of the supply voltage (SV) and SC signals, the FFC of the SC signal can be estimated with remarkable precision by the FFC of the SV signal, which can be easily extracted through the Fourier transform techniques. Thus, the FFC of the SC signal can be suppressed to a significant extent through subtracting the estimated FFC from the SC signal without obviously impacting other harmonics. In this way, the SBHs can be clearly identified from the spectrum of the filtered SC signal, even when the induction motor operates at relatively low slip, demonstrating the feasibility of utilizing the SBHs for slip estimation.
